Evaluating The Performance of Using Large Language Models to Automate Summarization of CT Simulation Orders in Radiation Oncology
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912694652633088 |
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| author | Cao, Meiyun Hu, Shaw Sharp, Jason Clouser, Edward Holmes, Jason Lam, Linda L. Ding, Xiaoning Toesca, Diego Santos Lindholm, Wendy S. Patel, Samir H. Vora, Sujay A. Wang, Peilong Liu, Wei |
| author_facet | Cao, Meiyun Hu, Shaw Sharp, Jason Clouser, Edward Holmes, Jason Lam, Linda L. Ding, Xiaoning Toesca, Diego Santos Lindholm, Wendy S. Patel, Samir H. Vora, Sujay A. Wang, Peilong Liu, Wei |
| contents | Purpose: This study aims to use a large language model (LLM) to automate the generation of summaries from the CT simulation orders and evaluate its performance.
Materials and Methods: A total of 607 CT simulation orders for patients were collected from the Aria database at our institution. A locally hosted Llama 3.1 405B model, accessed via the Application Programming Interface (API) service, was used to extract keywords from the CT simulation orders and generate summaries. The downloaded CT simulation orders were categorized into seven groups based on treatment modalities and disease sites. For each group, a customized instruction prompt was developed collaboratively with therapists to guide the Llama 3.1 405B model in generating summaries. The ground truth for the corresponding summaries was manually derived by carefully reviewing each CT simulation order and subsequently verified by therapists. The accuracy of the LLM-generated summaries was evaluated by therapists using the verified ground truth as a reference.
Results: About 98% of the LLM-generated summaries aligned with the manually generated ground truth in terms of accuracy. Our evaluations showed an improved consistency in format and enhanced readability of the LLM-generated summaries compared to the corresponding therapists-generated summaries. This automated approach demonstrated a consistent performance across all groups, regardless of modality or disease site.
Conclusions: This study demonstrated the high precision and consistency of the Llama 3.1 405B model in extracting keywords and summarizing CT simulation orders, suggesting that LLMs have great potential to help with this task, reduce the workload of therapists and improve workflow efficiency. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_16309 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating The Performance of Using Large Language Models to Automate Summarization of CT Simulation Orders in Radiation Oncology Cao, Meiyun Hu, Shaw Sharp, Jason Clouser, Edward Holmes, Jason Lam, Linda L. Ding, Xiaoning Toesca, Diego Santos Lindholm, Wendy S. Patel, Samir H. Vora, Sujay A. Wang, Peilong Liu, Wei Medical Physics Artificial Intelligence Purpose: This study aims to use a large language model (LLM) to automate the generation of summaries from the CT simulation orders and evaluate its performance. Materials and Methods: A total of 607 CT simulation orders for patients were collected from the Aria database at our institution. A locally hosted Llama 3.1 405B model, accessed via the Application Programming Interface (API) service, was used to extract keywords from the CT simulation orders and generate summaries. The downloaded CT simulation orders were categorized into seven groups based on treatment modalities and disease sites. For each group, a customized instruction prompt was developed collaboratively with therapists to guide the Llama 3.1 405B model in generating summaries. The ground truth for the corresponding summaries was manually derived by carefully reviewing each CT simulation order and subsequently verified by therapists. The accuracy of the LLM-generated summaries was evaluated by therapists using the verified ground truth as a reference. Results: About 98% of the LLM-generated summaries aligned with the manually generated ground truth in terms of accuracy. Our evaluations showed an improved consistency in format and enhanced readability of the LLM-generated summaries compared to the corresponding therapists-generated summaries. This automated approach demonstrated a consistent performance across all groups, regardless of modality or disease site. Conclusions: This study demonstrated the high precision and consistency of the Llama 3.1 405B model in extracting keywords and summarizing CT simulation orders, suggesting that LLMs have great potential to help with this task, reduce the workload of therapists and improve workflow efficiency. |
| title | Evaluating The Performance of Using Large Language Models to Automate Summarization of CT Simulation Orders in Radiation Oncology |
| topic | Medical Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2501.16309 |